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Auteur Xiaodong KANG |
Documents disponibles écrits par cet auteur (2)
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Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain / Yating MING ; Weixing ZHAO ; Rui FENG ; Yuanyue ZHOU ; Lijie WU ; Jia WANG ; Jinming XIAO ; Lei LI ; Xiaolong SHAN ; Jing CAO ; Xiaodong KANG ; Huafu CHEN ; Xujun DUAN in Molecular Autism, 14 (2023)
[article]
Titre : Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain Type de document : Texte imprimé et/ou numérique Auteurs : Yating MING, Auteur ; Weixing ZHAO, Auteur ; Rui FENG, Auteur ; Yuanyue ZHOU, Auteur ; Lijie WU, Auteur ; Jia WANG, Auteur ; Jinming XIAO, Auteur ; Lei LI, Auteur ; Xiaolong SHAN, Auteur ; Jing CAO, Auteur ; Xiaodong KANG, Auteur ; Huafu CHEN, Auteur ; Xujun DUAN, Auteur Article en page(s) : 41 p. Langues : Anglais (eng) Mots-clés : Child Humans Child, Preschool Diffusion Tensor Imaging/methods *Autistic Disorder/diagnostic imaging Brain/diagnostic imaging *White Matter/diagnostic imaging Cluster Analysis Index. décimale : PER Périodiques Résumé : OBJECTIVE: There has been increasing evidence for atypical white matter (WM) microstructure in autistic people, but findings have been divergent. The development of autistic people in early childhood is clouded by the concurrently rapid brain growth, which might lead to the inconsistent findings of atypical WM microstructure in autism. Here, we aimed to reveal the developmental nature of autistic children and delineate atypical WM microstructure throughout early childhood while taking developmental considerations into account. METHOD: In this study, diffusion tensor imaging was acquired from two independent cohorts, containing 91 autistic children and 100 typically developing children (TDC), aged 4-7 years. Developmental prediction modeling using support vector regression based on TDC participants was conducted to estimate the WM atypical development index of autistic children. Then, subgroups of autistic children were identified by using the k-means clustering method and were compared to each other on the basis of demographic information, WM atypical development index, and autistic trait by using two-sample t-test. Relationship of the WM atypical development index with age was estimated by using partial correlation. Furthermore, we performed threshold-free cluster enhancement-based two-sample t-test for the group comparison in WM microstructures of each subgroup of autistic children with the rematched subsets of TDC. RESULTS: We clustered autistic children into two subgroups according to WM atypical development index. The two subgroups exhibited distinct developmental stages and age-dependent diversity. WM atypical development index was found negatively associated with age. Moreover, an inverse pattern of atypical WM microstructures and different clinical manifestations in the two stages, with subgroup 1 showing overgrowth with low level of autistic traits and subgroup 2 exhibiting delayed maturation with high level of autistic traits, were revealed. CONCLUSION: This study illustrated age-dependent heterogeneity in early childhood autistic children and delineated developmental stage-specific difference that ranged from an overgrowth pattern to a delayed pattern. Trial registration This study has been registered at ClinicalTrials.gov (Identifier: NCT02807766) on June 21, 2016 ( https://clinicaltrials.gov/ct2/show/NCT02807766 ). En ligne : https://dx.doi.org/10.1186/s13229-023-00573-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=518
in Molecular Autism > 14 (2023) . - 41 p.[article] Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain [Texte imprimé et/ou numérique] / Yating MING, Auteur ; Weixing ZHAO, Auteur ; Rui FENG, Auteur ; Yuanyue ZHOU, Auteur ; Lijie WU, Auteur ; Jia WANG, Auteur ; Jinming XIAO, Auteur ; Lei LI, Auteur ; Xiaolong SHAN, Auteur ; Jing CAO, Auteur ; Xiaodong KANG, Auteur ; Huafu CHEN, Auteur ; Xujun DUAN, Auteur . - 41 p.
Langues : Anglais (eng)
in Molecular Autism > 14 (2023) . - 41 p.
Mots-clés : Child Humans Child, Preschool Diffusion Tensor Imaging/methods *Autistic Disorder/diagnostic imaging Brain/diagnostic imaging *White Matter/diagnostic imaging Cluster Analysis Index. décimale : PER Périodiques Résumé : OBJECTIVE: There has been increasing evidence for atypical white matter (WM) microstructure in autistic people, but findings have been divergent. The development of autistic people in early childhood is clouded by the concurrently rapid brain growth, which might lead to the inconsistent findings of atypical WM microstructure in autism. Here, we aimed to reveal the developmental nature of autistic children and delineate atypical WM microstructure throughout early childhood while taking developmental considerations into account. METHOD: In this study, diffusion tensor imaging was acquired from two independent cohorts, containing 91 autistic children and 100 typically developing children (TDC), aged 4-7 years. Developmental prediction modeling using support vector regression based on TDC participants was conducted to estimate the WM atypical development index of autistic children. Then, subgroups of autistic children were identified by using the k-means clustering method and were compared to each other on the basis of demographic information, WM atypical development index, and autistic trait by using two-sample t-test. Relationship of the WM atypical development index with age was estimated by using partial correlation. Furthermore, we performed threshold-free cluster enhancement-based two-sample t-test for the group comparison in WM microstructures of each subgroup of autistic children with the rematched subsets of TDC. RESULTS: We clustered autistic children into two subgroups according to WM atypical development index. The two subgroups exhibited distinct developmental stages and age-dependent diversity. WM atypical development index was found negatively associated with age. Moreover, an inverse pattern of atypical WM microstructures and different clinical manifestations in the two stages, with subgroup 1 showing overgrowth with low level of autistic traits and subgroup 2 exhibiting delayed maturation with high level of autistic traits, were revealed. CONCLUSION: This study illustrated age-dependent heterogeneity in early childhood autistic children and delineated developmental stage-specific difference that ranged from an overgrowth pattern to a delayed pattern. Trial registration This study has been registered at ClinicalTrials.gov (Identifier: NCT02807766) on June 21, 2016 ( https://clinicaltrials.gov/ct2/show/NCT02807766 ). En ligne : https://dx.doi.org/10.1186/s13229-023-00573-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=518 Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity / Wenwen ZHUANG in Autism Research, 16-8 (August 2023)
[article]
Titre : Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity Type de document : Texte imprimé et/ou numérique Auteurs : Wenwen ZHUANG, Auteur ; Hai JIA, Auteur ; Yunhong LIU, Auteur ; Jing CONG, Auteur ; Kai CHEN, Auteur ; Dezhong YAO, Auteur ; Xiaodong KANG, Auteur ; Peng XU, Auteur ; Tao ZHANG, Auteur Article en page(s) : p.1512-1526 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large-scale FNC as a feature to identify individuals with ASD. This study used a time-sliding window method to study the dynamic FNC (dFNC) on the resting-state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10-75 TRs (TR=2?s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10-fold cross-validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD. En ligne : https://doi.org/10.1002/aur.2974 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=510
in Autism Research > 16-8 (August 2023) . - p.1512-1526[article] Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity [Texte imprimé et/ou numérique] / Wenwen ZHUANG, Auteur ; Hai JIA, Auteur ; Yunhong LIU, Auteur ; Jing CONG, Auteur ; Kai CHEN, Auteur ; Dezhong YAO, Auteur ; Xiaodong KANG, Auteur ; Peng XU, Auteur ; Tao ZHANG, Auteur . - p.1512-1526.
Langues : Anglais (eng)
in Autism Research > 16-8 (August 2023) . - p.1512-1526
Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large-scale FNC as a feature to identify individuals with ASD. This study used a time-sliding window method to study the dynamic FNC (dFNC) on the resting-state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10-75 TRs (TR=2?s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10-fold cross-validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD. En ligne : https://doi.org/10.1002/aur.2974 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=510